Predictive Routing: General concepts and functionality FAQs
What is Genesys predictive routing?
Genesys predictive routing uses machine learning to match each interaction with the eligible agent most likely to achieve the queue’s chosen business outcome. Unlike standard routing, it considers the best agent customer fit, not just who is available first. Each queue optimizes one KPI at a time, such as Average Handle Time, Next Contact Avoidance, or Revenue.
What is the best way to explain GPR to agents and operational teams?
GPR does not rate agents from best to worst. It estimates which available agent is the best fit for each interaction and the selected queue. Different agents can be ranked first for different customers and interactions.
Is GPR simply routing interactions to the fastest agent?
No. GPR does not maintain a universal ranking of agents based on speed. Instead, it uses a queue-specific model to predict how each eligible agent is likely to affect the selected KPI for a particular interaction.
For example, Agent A may have the lowest overall Average Handle Time (AHT) in the queue, but GPR may rank Agent B higher for a specific interaction based on factors such as customer contact history, agent performance, and similar historical interactions.
Or, when Next Contact Avoidance is the selected KPI, GPR may favor an agent who is more likely to resolve the underlying issue, even if that agent does not have the lowest AHT.
GPR uses multiple data points to determine the predicted agent-interaction fit rather than simply ranking agents by speed.
How does predictive routing differ from standard skill-based routing?
Predictive routing does not replace skills based routing. It works on top of it. Skills continue to decide who is eligible for an interaction, and predictive routing decides which of those eligible agents is the best fit.
Standard skills based routing is deterministic. It filters to the agents who hold the required skills at the required proficiency, then selects among them using a fixed rule such as longest idle or agent priority. Every agent who clears the skill bar is treated as interchangeable.
Predictive routing keeps that eligibility filter and then ranks within it. When skill matching is enabled on the queue, the required ACD skills still gate who is eligible. Predictive routing scores those eligible agents on how likely each one is to achieve the queue’s selected KPI for that specific interaction, and tries the highest scoring agents first.
The practical differences:
- Eligibility compared with ranking: skills determine who may handle the interaction, predictive routing determines which of them is the best fit.
- Fixed rule compared with a learned model: skills based routing applies the rule you configured. Predictive routing builds a model from the queue’s historical outcomes and retrains as new data arrives.
- Uniform compared with per interaction: a skills rule treats all qualifying agents alike. Predictive routing can rank different agents first for different customers and interaction types.
- Per queue scope: each predictive routing queue has its own model optimizing one KPI. There is no global cross queue ranking of agents.
- Fallback: if no suitable agent is found within the timeout, the interaction routes using your standard method, so behavior returns to what you have today rather than failing.
Adopting predictive routing does not require you to restructure your existing skills or proficiency assignments. They continue to serve the same purpose. To turn skill matching on or off per queue, see Configure skill matching.
Routing admin view: your existing skill model stays as it is. Enabling predictive routing changes how an agent is chosen from the eligible pool, not who is in the pool.
LoB owner or workforce manager view: the change is from “anyone qualified, whoever has been free longest” to “the qualified agent most likely to deliver the outcome selected for this queue.”
Does predictive routing work with bullseye routing?
No. Predictive routing does not support bullseye routing. However, you can use predictive routing with conditional group activation (CGA).
CGA dynamically adjusts the pool of eligible agents based on configured conditions, while predictive routing optimizes agent selection within the currently active pool.
For example, you can configure CGA to activate an overflow group when the estimated wait time (EWT) of the queue exceeds five minutes. When the condition is no longer met, that is the EWT falls below five minutes, CGA removes the overflow group. Predictive routing continues to select the best-fit agent from the eligible agents throughout this process.
In this configuration, CGA determines which agent groups are active, while predictive routing determines which agent is the best fit for the interaction from within the pool.
How does predictive routing select agents?
GPR can use multiple categories of agent, customer, interaction, and performance data. The features that the model uses can vary based on the queue, media type, available data, and selected KPI.
Genesys Cloud uses internal data sources, including the agent directory and analytics data. GPR can also use optional data from sources such as web surveys, sentiment analysis, external contacts, agent profile fields, participant data, and WEM external metrics.
Examples of data that GPR can use include:
| Data category | Examples |
|---|---|
Agent profile – Identifies agents’ experience, specialization and organizational context | Skills, language, department, tenure or hire date, employee type, and certifications |
Agent performance – Identifies agents past performance in terms of stronger outcomes in comparable circumstances
| Historical queue-level AHT, after-call work, transfer patterns, and other interaction outcomes |
Customer history – Provides context about the customer’s relationship with the contact center and the likely complexity of the interaction
| Repeat-contact history, number of previous contacts, and previous queue interactions |
Interaction and operational context – Helps determine the current operating situation
| Queue, media type, time period, interaction priority, waiting time, and available-agent conditions |
Customer or business attributes (Optional data) – Adds business context when these attributes are consistently supplied as participant data
| VIP indicator, contract type, intent code, or churn propensity supplied as participant data |
Experience data (Optional data) – Can help enrich the patterns available to the model when enough usable data exists
| Survey results, sentiment, and external contact information |
Workforce metrics (Optional data) – Can enhance the agent profile with performance measures maintained outside the core interaction data
| External agent performance or specialization metrics supplied through WEM Gamification integrations |
For optional attributes, Genesys recommends broad data coverage and consistent values. The model may not use attributes that contain insufficient data or values that are highly unique.
Can customers see which features influence predictive routing decisions?
Yes. Genesys Cloud provides model explainability information for predictive routing queues. The Predictive Model view shows the most influential features and groups them into agent, customer, and other feature categories.
Feature importance indicates how useful a feature is to the model when making predictions. However, it does not indicate that the feature caused an outcome. For example, low after-call work may be associated with lower AHT, but reducing after-call work alone does not necessarily result in lower AHT.
What factors influence predictive routing decisions?
Predictive routing decisions are based on a predicted agent score calculated for each interaction in real time. AI models use the following as inputs:
- Past interaction history
- Available agent profiles
- Participant data
Does predictive routing take skill levels into account, or do skill level reviews need to happen separately?
Yes. Administrators can turn skill matching on or off for individual queues configured to use the predictive routing method. Skill matching allows an organization to factor agent skill settings into routing decisions for inbound interactions, and includes all non language ACD skills associated with queue members. See Configure skill matching.
Can customers enhance predictive routing with external data?
Yes. Customers can inject external data as participant data at runtime routing, which can then be used as features for the predictive routing machine learning models.
Is customer intent required for predictive routing?
No. Predictive routing does not require intent detection or classification, and there is no prerequisite to deploy a bot, an intent model, or IVR intent capture before enabling it. The model learns from data the platform already holds for the queue: historical interaction outcomes, agent profile and performance, the customer’s previous contact history, and the operational context of the interaction.
Intent is optional enrichment. If an intent code is already derived elsewhere, for example from a bot, a menu selection, or a classification step in the flow, it can be supplied as participant data, and the model may use it as a feature. Two conditions apply, as they do to any optional attribute:
- Coverage must be broad. An attribute present on only a small proportion of interactions gives the model little to learn from.
- Values must be consistent. Highly unique or free text values are generally not usable as features.
Whether a supplied intent attribute makes a difference can be verified afterward. The Predictive Model view shows which features are influencing predictions, so administrators can confirm whether the attribute is being used rather than assuming it is.
For a routing admin: no flow redesign is needed to begin. If an intent attribute is already set, it can be passed as participant data, and the Predictive Model view can be checked later to confirm whether the model uses it.
For a workforce manager: predictive routing does not depend on an intent or NLU project being completed first. It can start from the interaction history already available.
Can the model be guarded against favoring calls that are too short, for example, assuming that one to two minute calls are optimal?
Yes. The model scores agents based on how likely they are to achieve the selected KPI, not on call length alone. For AHT, the model learns from historical patterns and recognizes that extremely short calls, such as transfers or misroutes, do not represent true success, so it does not prioritize them. See How the AI model scores agents for Predictive Routing.
Could predictive routing pull in quality evaluation scores from managers into the decision model?
No. Quality evaluation scores are not consumed by GPR models automatically.
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